# Automated Imbalanced Learning

arXiv: https://arxiv.org/abs/2211.00376 · HTML: https://mlsanity.com/paper/2211.00376/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [A System for Massively Parallel Hyperparameter Tuning](https://mlsanity.com/paper/1810.05934/) (2018)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Generative Adversarial Minority Oversampling](https://mlsanity.com/paper/1903.09730/) (2019)
- [MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler](https://mlsanity.com/paper/2010.08830/) (2020)
- [Radial-Based Undersampling for Imbalanced Data Classification](https://mlsanity.com/paper/1906.00452/) (2019)
- [GAMA: a General Automated Machine learning Assistant](https://mlsanity.com/paper/2007.04911/) (2020)
- [Online AutoML: An adaptive AutoML framework for online learning](https://mlsanity.com/paper/2201.09750/) (2022)
- [CSMOUTE: Combined Synthetic Oversampling and Undersampling Technique for Imbalanced Data Classification](https://mlsanity.com/paper/2004.03409/) (2020)

## Built on by (most cited first, 3 of 3)

- [The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems](https://mlsanity.com/paper/2307.00157/) (2023)
- [AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data](https://mlsanity.com/paper/2508.02625/) (2025)
- [Robustness of AutoML on Dirty Categorical Data](https://mlsanity.com/paper/2602.00412/) (2026)

## Abstract (arXiv)

Automated Machine Learning has grown very successful in automating the time-consuming, iterative tasks of machine learning model development. However, current methods struggle when the data is imbalanced. Since many real-world datasets are naturally imbalanced, and improper handling of this issue can lead to quite useless models, this issue should be handled carefully. This paper first introduces a new benchmark to study how different AutoML methods are affected by label imbalance. Second, we propose strategies to better deal with imbalance and integrate them into an existing AutoML framework. Finally, we present a systematic study which evaluates the impact of these strategies and find that their inclusion in AutoML systems significantly increases their robustness against label imbalance.